Active Matching

  • Margarita Chli
  • Andrew J. Davison
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5302)


In the matching tasks which form an integral part of all types of tracking and geometrical vision, there are invariably priors available on the absolute and/or relative image locations of features of interest. Usually, these priors are used post-hoc in the process of resolving feature matches and obtaining final scene estimates, via ‘first get candidate matches, then resolve’ consensus algorithms such as RANSAC. In this paper we show that the dramatically different approach of using priors dynamically to guide a feature by feature matching search can achieve global matching with much fewer image processing operations and lower overall computational cost. Essentially, we put image processing into the loop of the search for global consensus. In particular, our approach is able to cope with significant image ambiguity thanks to a dynamic mixture of Gaussians treatment. In our fully Bayesian algorithm, the choice of the most efficient search action at each step is guided intuitively and rigorously by expected Shannon information gain. We demonstrate the algorithm in feature matching as part of a sequential SLAM system for 3D camera tracking. Robust, real-time matching can be achieved even in the previously unmanageable case of jerky, rapid motion necessitating weak motion modelling and large search regions.


Mutual Information Template Match Search Region True Match Image Processing Operation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Supplementary material

978-3-540-88682-2_7_MOESM1_ESM.avi (17.6 mb)
Supplementary material (18,040 KB)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Margarita Chli
    • 1
  • Andrew J. Davison
    • 1
  1. 1.Imperial College LondonLondonUK

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